Predicting user navigation events in a browser using directed graphs

ABSTRACT

A method and system for predicting a next navigation event are described. Aspects of the disclosure minimize the delay between a navigation event and a network response by predicting the next navigation event. The system and method may then prerender content associated with the next navigation event. For example, the method and system may predict a likely next uniform resource locator during web browsing to preemptively request content from the network before the user selects the corresponding link on a web page. The methods describe a variety of manners of predicting the next navigation event, including examining individual and aggregate historical data, text entry prediction, and cursor input monitoring.

BACKGROUND

The advent of the World Wide Web has placed more information at thefingertips of today's users than ever before. Various websites cater tonearly every need and interest, providing access to referenceinformation, business and financial documents, social networking, andmore. Widespread broadband Internet access provides faster access tothese sites than ever before.

However, as fast as current high-speed Internet services are, the act ofbrowsing the web is not instantaneous. When a user selects a link on apage or enters a uniform resource locator (URL) in a text field, thereis a delay while data is requested from the host, sent to the client,and rendered in the browser. The user may be idle while waiting fortheir requested site to load. While high-speed Internet access may limitthis delay to a few seconds, even this short delay can add up tothousands of man-hours of lost productivity each year.

BRIEF SUMMARY

A method and system for predicting user navigation events are described.Aspects of the disclosure minimize the delay in accessing web content bypredicting a user navigation event on a web page. The navigation eventmay be predicted by various indicators, including but not limited to auser's navigation history, aggregate navigation history, text entrywithin a data entry field, or a mouse cursor position. Users can beprovided with an opportunity to opt in or out of functionality that maycollect personal information about them. In addition, certain data canbe anonymized and aggregated before it is stored or used, such thatpersonally identifiable information is removed.

Aspects of the disclosure provide a computer-implemented method forpredicting a user navigation event. The method may include storing a setof navigation data, the navigation data comprising at least one directedgraph, where at least one vertex of the directed graph comprises atleast one navigation event and an edge of the directed graph is weightedby a count value representing a number of times a navigation hasoccurred from a source vertex to a destination vertex coupled thereto bythe edge, identifying a current navigation history, the currentnavigation history comprising one or more previous navigation eventsaccessed in a browser, mapping the current navigation history, using aprocessor, to the at least one directed graph within the set ofnavigation data, determining a confidence value for a given navigationevent using the at least one directed graph, and identifying the givennavigation event as a likely navigation event based on the confidencevalue, such that the likely navigation event is configured to beutilized by the browser to assist with navigation operations. The atleast one directed graph may be keyed to at least one previousnavigation event in the current navigation history. The at least oneprevious navigation event may be identified in an order each of the atleast one previous navigation events were visited. The method may alsoinclude identifying n previous navigation events in the currentnavigation history, and determining which of the at least one directedgraphs is keyed to the n previous navigation events. The confidencevalue may be weighted by at least a depth of the at least one directedgraph. The weight of the directed graph may be determined by a formulagraph_depth^(N)*samples_in_graph, wherein graph_depth is the depth,samples_in_graph is a number of navigation events stored in the graph,and N is a numerical value used to adjust the significance of thegraph_depth term in the weight calculation. The method may also includeprerendering the likely navigation event. The browser may be a webbrowser.

Aspects of the disclosure also provide a non-transitorycomputer-readable storage medium comprising instructions that, whenexecuted by a processor, cause the processor to perform a method. Themethod executed by the processor may include storing a set of navigationdata, the navigation data comprising at least one directed graph, whereat least one vertex of the directed graph comprises at least onenavigation event and an edge of the directed graph is weighted by acount value representing a number of times a navigation has occurredfrom a source vertex to a destination vertex coupled thereto by theedge, identifying a current navigation history, the current navigationhistory comprising one or more previous navigation events accessed in abrowser, mapping the current navigation history to the at least onedirected graph within the set of navigation data, determining aconfidence value for a given navigation event using the at least onedirected graph, and identifying the given navigation event as a likelynavigation event based on the confidence value, such that the likelynavigation event is configured to be utilized by the browser to assistwith navigation operations. The at least one directed graph may be keyedto at least one previous navigation event in the current navigationhistory. The at least one previous navigation event may be identified inan order each of the at least one previous navigation events werevisited. The method executed by the processor may also includeidentifying n previous navigation events in the current navigationhistory, and determining which of the at least one directed graphs iskeyed to the n previous navigation events. The confidence value may beweighted by a depth of the at least one directed graph. The methodexecuted by the processor may also include prerendering the likelynavigation event.

Aspects of the disclosure also provide a processing system forpredicting a user navigation event. The processing system may include atleast one processor, and a memory, coupled to the processor, for storinga set of navigation data, the navigation data comprising at least onedirected graph, where at least one vertex of the directed graphcomprises at least one navigation event and an edge of the directedgraph is weighted by a count value representing a number of times anavigation has occurred from a source vertex to a destination vertexcoupled by the edge. The processor may be configured to identify acurrent navigation history, the current navigation history comprisingone or more previous navigation events accessed in a browser, map thecurrent navigation history to the at least one directed graph within theset of navigation data, determine a confidence value for a givennavigation event using the at least one directed graph, and identify thegiven navigation event as a likely navigation event based on theconfidence value, such that the likely navigation event is configured tobe utilized by the browser to assist with navigation operations. Thebrowser may be configured to prerender the likely navigation event. Theat least one directed graph may be keyed to at least one previousnavigation event in the current navigation history. The at least oneprevious navigation event may be identified in an order each of the atleast one previous navigation events were visited. The processor may befurther configured to identify n previous navigation events in thecurrent navigation history, and determine which of the at least onedirected graphs is keyed to the n previous navigation events. Theconfidence value may be weighted by a depth of the at least one directedgraph. The browser may be a web browser.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram depicting an example of a server incommunication with example client devices in accordance with aspects ofthe disclosure.

FIG. 2 is block diagram depicting an example of a computing device inaccordance with aspects of the disclosure.

FIG. 3 is a flow diagram depicting an example of a method forprerendering a web page based upon a predicted navigation event inaccordance with aspects of the disclosure.

FIG. 4 is a flow diagram depicting an example of a method for predictinga navigation event based on a client navigation history in accordancewith aspects of the disclosure.

FIG. 5 is a flow diagram depicting an example of a method for computinga confidence value for a URL using a client navigation history inaccordance with aspects of the disclosure.

FIG. 6 is a flow diagram depicting an example of a method for predictinga navigation event based on an aggregate navigation history inaccordance with aspects of the disclosure.

FIG. 7 is a flow diagram depicting an example of a method for computinga confidence value for a URL using an aggregate navigation history inaccordance with aspects of the disclosure.

FIG. 8A is a flow diagram depicting an example of a method forpredicting a navigation event based on an aggregate navigation historyusing hash values to anonymously manage link data in accordance withaspects of the disclosure.

FIG. 8B is an illustration of an example of a web browser employing anexample method for predicting a user navigation event based on anaggregate navigation history in accordance with aspects of thedisclosure.

FIG. 9 is an illustration of a directed graph for storing a navigationhistory in accordance with aspects of the disclosure.

FIG. 10 is an illustration of an example of a method for building adirected graph in accordance with aspects of the disclosure.

FIG. 11 is an illustration of an example of a method for predicting anavigation event using a directed graph in accordance with aspects ofthe disclosure.

DETAILED DESCRIPTION

Embodiments of a system and method for predicting user navigation eventsare described herein. Aspects of this disclosure minimize the delaybetween a navigation event and a network response by predicting the nextnavigation event. The system and method may prerender content associatedwith the next navigation event. For example, the method and system maypredict a likely next uniform resource locator during web browsing topreemptively request content from the network before the user selectsthe corresponding link, thus reducing or eliminating the wait time whena user selects a hyperlink on a web page. Various methods describing avariety of manners of predicting the next navigation event, includingexamining individual and aggregate historical data, text entryprediction, and cursor input monitoring, are described. Aspects of thedisclosure also relate to the prediction of the immediate usernavigation (e.g., the next link the user is likely to select whenviewing a particular web page, such as within the next 30 seconds, thenext minute, or the next 5 minutes).

As shown in FIG. 1, an example system 102 in accordance with oneembodiment includes a server 104 in communication (via a network 112)with one or more client devices 106, 108, 110 displaying web browserinterfaces 114, 116, 118, respectively.

The client devices 106, 108, 110 are configured to perform prerenderingoperations during the execution of a web browser application. The server104 may transmit navigation history data to the client devices 106, 108,110, to enable prediction of a next navigation event. In some aspects,the client devices 106, 108, 110 determine a next navigation event usinga local navigation history and generate a web request to the server 104to prerender the content associated with the next navigation event. Forexample, the user of the client device 106 may browse to a web pagelocated at “www.a.com” as displayed on the web browser interface 112.That page includes content selectable by the user. Based on the user'snavigation history, the client device 106 may determine which of theselectable content the user is likely to select, and then prerender thecontent associated with the selectable content by requesting the contentfrom the server 104.

As another example, the client device 108 may display www.a.com within abrowser 114. The client device 108 may receive an aggregate set ofnavigation statistics from the server 104, and then determine whichselectable content the user is likely to select based upon the aggregateset of navigation statistics. As yet another example, the client device110 may display www.a.com within a browser 116. The client device 108may determine which selectable content the user is likely to selectbased upon a cursor position within the browser 114.

While the concepts described herein are generally discussed with respectto a web browser, aspects of the disclosure can be applied to anycomputing node capable of managing navigation events over a network,including a server 104.

The client devices 106, 108, 110 may be any device capable managing datarequests via a network 112. Examples of such client devices includepersonal computers, personal digital assistants (“PDA”): tablet PCs,netbooks, laptops, etc. Indeed, client devices in accordance with thesystems and methods described herein may comprise any device operativeto process instructions and transmit data to and from humans and othercomputers including general purpose computers, network computers lackinglocal storage capability, etc.

The client devices 106, 108, 110 are operable to predict navigationevents to assist in data access via the network 112. For example, theclient devices may predict a likely navigation event to facilitateprerendering of a web page in order to improve the user's browsingexperience. In some aspects, the server 104 provides navigation datathat may be used by the client devices 106, 108, 110 to predict a likelynavigation event (see FIGS. 6-8). In some aspects, the client devices106, 108, 110 predict a likely navigation event using local data. (seeFIGS. 3-5, 9-11).

The network 112, and the intervening nodes between the server 104 andthe client devices 106, 108, 110, may comprise various configurationsand use various protocols including the Internet, World Wide Web,intranets, virtual private networks, local Ethernet networks, privatenetworks using communication protocols proprietary to one or morecompanies, cellular and wireless networks (e.g., Wi-Fi), instantmessaging, hypertext transfer protocol (“HTTP”) and simple mail transferprotocol (“SMTP”), and various combinations of the foregoing. It shouldbe appreciated that a typical system may include a large number ofconnected computers.

Although certain advantages are obtained when information is transmittedor received as noted above, other aspects of the system and method arenot limited to any particular manner of transmission of information. Forexample, in some aspects, information may be sent via a medium such asan optical disk or portable drive. In other aspects, the information maybe transmitted in a non-electronic format and manually entered into thesystem.

Although some functions are indicated as taking place on the server 104and other functions are indicated as taking place on the client devices106, 108, 110, various aspects of the system and method may beimplemented by a single computer having a single processor. It should beappreciated that aspects of the system and method described with respectto the client device may be implemented on the server, and vice-versa.

FIG. 2 is a block diagram depicting an example of a computing device200, such as one of the client devices 106, 108, 110 described withrespect to FIG. 1. The computing device 200 may include a processor 204,a memory 202 and other components typically present in general purposecomputers. The memory 202 may store instructions and data that areaccessible by the processor 204. The processor 204 may execute theinstructions and access the data to control the operations of thecomputing device 200.

The memory 202 may be any type of memory operative to store informationaccessible by the processor 120, including a computer-readable medium,or other medium that stores data that may be read with the aid of anelectronic device, such as a hard-drive, memory card, read-only memory(“ROM”), random access memory (“RAM”), digital versatile disc (“DVD”) orother optical disks, as well as other write-capable and read-onlymemories. The system and method may include different combinations ofthe foregoing, whereby different portions of the instructions and dataare stored on different types of media.

The instructions may be any set of instructions to be executed directly(such as machine code) or indirectly (such as scripts) by the processor204. For example, the instructions may be stored as computer code on acomputer-readable medium. In that regard, the terms “instructions” and“programs” may be used interchangeably herein. The instructions may bestored in object code format for direct processing by the processor 204,or in any other computer language including scripts or collections ofindependent source code modules that are interpreted on demand orcompiled in advance. Functions, methods and routines of the instructionsare explained in more detail below (see FIGS. 3-11).

Data may be retrieved, stored or modified by processor in accordancewith the instructions. For instance, although the architecture is notlimited by any particular data structure, the data may be stored incomputer registers, in a relational database as a table having aplurality of different fields and records, Extensible Markup Language(“XML”) documents or flat files. The data may also be formatted in anycomputer readable format such as, but not limited to, binary values orUnicode. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 204 may be any suitable processor, such as variouscommercially available general purpose processors. Alternatively, theprocessor may be a dedicated controller such as an application-specificintegrated circuit (“ASIC”).

Although FIG. 2 functionally illustrates the processor and memory asbeing within a single block, it should be understood that the processor204 and memory 202 may comprise multiple processors and memories thatmay or may not be stored within the same physical housing. Accordingly,references to a processor, computer or memory will be understood toinclude references to a collection of processors, computers or memoriesthat may or may not operate in parallel.

The computing device 200 may be at one node of the network and beoperative to directly and indirectly communicate with other nodes of thenetwork. For example, the computing device 200 may comprise a web serverthat is operative to communicate with client devices via the networksuch that the computing device 200 uses the network to transmit anddisplay information to a user on a display of the client device.

In some examples, the system provides privacy protections for the clientdata including, for example, anonymization of personally identifiableinformation, aggregation of data, filtering of sensitive information,encryption, hashing or filtering of sensitive information to removepersonal attributes, time limitations on storage of information, and/orlimitations on data use or sharing. Data can be anonymized andaggregated such that individual client data is not revealed.

In order to facilitate the navigation event prediction operations of thecomputing device 200, the memory 202 may further comprise a browser 206,a navigation prediction module 208, a prerender module 210, a clientnavigation history 212, and an aggregate navigation history 214.Although a number of discrete modules (e.g., 206, 208, 210, 212 and 214)are identified in connection with FIG. 2, the functionality of thesemodules can overlap and/or exist in a fewer or greater number of modulesthan what is shown, with such modules residing at one or more processingdevices, which may be geographically dispersed. The browser 206 providesfor the display of network content, such as a web page 216, a set ofsearch results, or any other type of network data, to a user of theclient device by sending and receiving data across a computer network.The web page 216 may be received in response to a network request, suchas a Hypertext Transfer Protocol (HTTP) GET request. The web page 216may be provided in a markup language, such as Hypertext Markup Language(HTML). The web page 216 may also include various scripts, data, forms,and the like, including interactive and executable content such as ADOBEFLASH content, JAVASCRIPT content, and the like.

The browser 206 may further comprise a prerendered web page 218. Theprerendered web page 218 represents a web page that was requested andaccessed by the prerender module 210 in response to a predictednavigation event provided by the navigation prediction module 208. Inthe event the user inputs a navigation event as predicted by theprediction module 208, the browser 206 may swap the prerendered web page218 with the web page 216, thus providing the content associated withthe navigation event without the need to send another network request.In some aspects, the swap may occur before the prerendered web page 218has finished loading. In such cases, the partially loaded prerenderedweb page 218 may be swapped in to continue loading as the active page.

The memory 202 may further comprise a prerender module 210 to performfetching of a next web page as identified by the navigation predictionmodule 208. The prerender module 210 sends a network request for the webpage identified to be the likely next navigation destination that theuser will select. The web page received in response to this request isthen stored in the browser 206 as the prerendered web page 218. In someaspects, the web page request generated by the prerender module 210 isidentical to a standard web page request. In some aspects, the web pagerequest generated by the prerender module 210 comprises certain featuresto facilitate the prerender process.

The memory 202 may also store a client navigation history 212 and anaggregate navigation history 214. The client navigation history 212comprises a set of navigation events associated with past activity ofthe browser 206. The client navigation history 212 may track a set ofvisited URLs, also known as a “clickstream,” which tracks an order inwhich the user typically visits URLs (e.g., when the user visits a newswebsite, they tend to next select a URL corresponding to the top storyof the day), a set of access times associated with the URLs, and thelike. In some aspects, the client navigation history 212 comprises a setof URLs and a frequency with which the user has visited each URL. Insome aspects, the client navigation history comprises a set of URLpairs, representing a source URL and a destination URL. The aggregatenavigation history 214 may comprise similar data as the clientnavigation history 212, but keyed to multiple users rather than a singleuser. As with the client navigation history 212, the aggregatenavigation history 214 may be stored as a set of URLs and a frequencyfor each, or a set of URL pairs representing a transition from a sourceURL to a destination URL.

The client navigation history 212 and aggregate navigation history 214may represent data collected using one or more browser add-ons, scripts,or toolbars. In some aspects, the client navigation history 212 and/oraggregate navigation history 214 are maintained on a remote server, suchas the server 104, and provided to the computing device 200. Thecomputing device 200 may maintain separate records to facilitate thepredicting of a next likely navigation event, or it may act in concertwith remotely stored data. In some aspects, only aggregate navigationhistory 214 pertaining to the particular web page the user is currentlyviewing is provided to the computing device 200 (see FIGS. 6 and 8).

As described above, the aggregate navigation history data 214 can bemaintained in an anonymous fashion, with privacy protections for theindividual client data that comprises the aggregate navigation history,including, for example, anonymization of personally identifiableinformation, aggregation of data, filtering of sensitive information,encryption, hashing or filtering of sensitive information to removepersonal attributes, time limitations on storage of information, and/orlimitations on data use or sharing. The aggregate navigation history 214data can be anonymized and aggregated such that individual client datais not revealed.

FIG. 3 is a flow diagram depicting an example method 300 forprerendering a web page based upon a predicted navigation event inaccordance with aspects of the disclosure. Aspects of the method 300operate to identify one or more likely navigation destinations from aset of navigation indicators, and then prerender the identifiednavigation destinations. The method 300 may be performed by a computingdevice, such as the computing device 200, to eliminate delays in theuser web browsing experience by prerendering web pages that areidentified as likely navigation targets by the user. For example, themethod 300 may be performed by elements of the browser 206, thenavigation prediction module 208, and the prerender module 210 actingtogether. While aspects of the method 300 are described with respect tothe computing device 200, the method 300 may also be performed by theserver 104, or any device with hardware and/or software designed toaccept instructions.

At stage 302, the computing device 200 receives one or more indicatorsof navigational intent. Navigational intent may be any action that wouldtend to indicate that the user will generate a particular networkrequest, such as a request for a particular web page. For example, theindicators may provide metrics by which to determine what the particularrequest will be, such as a confidence value. For example, the user maynavigate to a certain web page, from which they generally navigate toanother certain web page based upon their browsing history, or the usermay move his mouse cursor towards a particular hyperlink embedded withina web page. In some aspects, the indicator is received from a remoteserver, such as a search engine that embeds an indicator within searchresults, indicating that most users that submit a particular searchquery select a particular search result.

At stage 304, after receiving the indicator of navigational intent, thecomputing device 200 attempts to predict the most likely navigationevent. In short, the computing device 200 makes a best guess of to wherethe user is likely to navigate next, based upon the indicator. Methodsof performing this prediction are described below (see FIGS. 4-11).

At stage 306, the computing device 200 prerenders the content from thepredicted next navigation event as determined at stage 304. Theprerendering process may include storing a prerendered web page within abrowser, such as the prerendered web page 218. The computing device 200may prerender a single web page predicted as the most likely navigationevent, or the computing device 200 may prerender multiple pages. In someaspects, the computing device 200 determines the number of pages toprerender based upon one or more system capabilities of the computingdevice 200, such as available system resources, available networkbandwidth, processor speed, installed memory, and the like. In someaspects, the number of pages to prerender may be configurable in one ormore user settings. After prerendering the content associated with thenavigation event(s), the method 300 ends.

Multiple methods for predicting a next navigation event are providedbelow. While each method is described separately, it should beappreciated that aspects of the methods may be combined to improvenavigation prediction operations.

FIG. 4 is a flow diagram depicting an example method 400 for predictinga navigation event based on a client navigation history in accordancewith aspects of the disclosure. The method 400 provides for storing anavigation history for a user, and predicting a next navigation eventbased upon a navigation history of a particular user. As above, themethod 400 may be performed by a computing device such as the computingdevice 200. In particular, the method 400 may be performed by anavigation prediction module executing on a processor, such as thenavigation prediction module 208.

At stage 402, the computing device 200 tracks a user navigation history.For example, the computing device 200 may store records of web pagesvisited by the user, such as the browsing history commonly maintained inweb browsers. The browsing history may comprise the URLs of the webpages visited by the user, the order in which the URLs were visited, andthe manner in which the user selected the URL (e.g., whether the URL wasa clicked hyperlink, typed into an address bar, a redirect operationfrom another web page, etc.).

At stage 404, the computing device 200 determines a most likelynavigation event or events based upon the user navigation history. Themost likely navigation events may be determined by identifying theglobally most visited pages for the user, or the navigation events maybe associated with one or more current criteria. For example, thecomputing device 200 may examine the user's navigation history todetermine that, when the user is viewing a particular news web page,they almost always select a link to the top news story on that page, orthat when the user first opens the browser in the morning, they arelikely to navigate to their bank account page to check their dailybalance. The computing device 200 may employ various rules, heuristics,and filters to determine the most likely navigation event from the userhistory. The computing device 200 may associate each navigation eventwith a particular confidence value, indicating the likelihood that theuser will select each navigation event. These confidence values may thenbe used to sort the navigation events to determine the most likelynavigation event. A method to determine a confidence value for a givennavigation event is described further below (see FIG. 5).

At stage 406, the computing device 200 reports the most likelynavigation event as the predicted navigation event. For example, thesepredicted most likely navigation event may then be employed by themethod described above (see FIG. 3) to facilitate prerendering of theweb pages associated with the most likely navigation event.

FIG. 5 is a flow diagram depicting an example method 500 for computing aconfidence value for a URL using a user navigation history in accordancewith aspects of the disclosure. The method 500 is operable to tracknavigation events input by the user and to maintain a frequency valuefor each stored event. The method 500 may be employed to build a clientnavigation history as used by the method 400, and stored on thecomputing device 200 as the client navigation history 212.

At stage 502, the computing device 200 tracks the selection of aparticular URL. For example, the user may type a URL for a news siteinto the browser, or click a link on a page. The computing device 200may monitor the navigation events using functionality built into thebrowser 206, through a browser extension such as a plug-in or toolbar,or via a third party application executing in tandem with the browser.

At stage 504, the computing device 200 increments a frequency valueassociated with the URL selected at stage 502. For example, thecomputing device 200 may track a frequency value associated with eachURL selected by a user. The frequency value is a data metric used torank a number of visits to a particular web site or the number of timesa particular navigation event is selected. In response to a selectionoperation, the computing device 200 may increment the frequency valueassociated with the URL, for example by 1.0, 5.0, 10.0, 0.5, or anyother value. The frequency value associated with the URL represents howoften the user has selected the particular URL, and thus is an indicatorof how likely the user is to select the URL in the future.

At stage 506, the computing device 200 time decays the stored frequencyvalues for the URLs after a given “sweep interval”. Decaying the URLfrequency values in this manner allows for current browsing habits to bemore heavily weighted than previous browsing habits. As an example, thecomputing device 200 may execute the sweep every 30 seconds, everyminute, or every 5 minutes during which the user has selected at leastone URL. The sweep interval may be conducted in response to theselection of at least one URL during a particular sweep interval toensure that the navigation history values are not decayed below athreshold value during periods where the user is inactive. The sweep maydecay the stored frequency value associated with the URL by a particularvalue, such as 0.99, 0.5, or 1.0, or by a percentage value, such as 5%,10%, or 50%. Once the value associated with the URL drops below a giventhreshold, for example, 0.3, 1.0, or 5.0, the URL may be removed fromthe list of possible navigation destinations to avoid the list growingtoo large. After conducting the decay process, the frequency values forthe URLs may be persisted to a local storage on the computing device200, or sent to a remote storage such as provided by the server 104.

At stage 508, the stored frequency values may be used to determine therelative frequency with which the user visits particular web sites. Thefrequency value thus provides a basis from which a confidence valueassociated with a navigation event leading to each web site may bederived. In some aspects, the frequency value itself may be provided asthe confidence value. In some aspects, the confidence value isdetermined by comparing a frequency value for a particular web page withthe entire user navigation history. For example, the navigation eventwith the higher frequency value may be associated with a particularpercentage confidence value, the second highest frequency value a lowerpercentage, and the like. In some aspects, the confidence value isdetermined by frequency value by the total number of logged navigationevents. For example, the frequency value of a particular URL may bedivided by the sum of all frequency values to determine a confidencevalue.

For example, a user may be in the process of buying a home, and thusregularly checking financial and banking websites for mortgage rates.During this time, these financial and banking sites would have highvalues and thus be more likely to be prerendered, thus improving theuser experience while searching for a mortgage rate. After completingthe home purchase process, the user is likely to lose interest in day today rate fluctuations, and thus it is no longer optimal to prerenderthese websites, since the user is unlikely to visit them. As such,providing for a time decay value allows these sites to fall off of thelist over time.

FIG. 6 is a flow diagram depicting an example method 600 for predictinga navigation event based on an aggregate navigation history inaccordance with aspects of the disclosure. The method 600 is operable totrack navigation events voluntarily submitted by users to determinelikely navigation patterns. The navigation patterns are then analyzed,such as by a server 104, and supplied to the user to facilitatenavigation event prediction during the browsing process. For example, aserver, such as the server 104, may send updates to a computing device,such as the computing device 200, as the user browses to differentpages, to provide information on which link displayed on a given page ismost likely to be selected based on the aggregate navigation history.

At stage 602, the server 104 receives a set of navigation informationcomprising a browsing history. The browsing history is preferablyprovided by using an “opt-in/out” method, where the user specificallyenables (or disables) reporting functionality to provide elements oftheir browsing history to the server 104. In addition, personallyidentifying data can be anonymized and aggregated before it is stored orused, such that no personal information is stored or accessible. Abrowsing history may be tracked and provided to the server 104 via abrowser plug-in or toolbar installed on the user's computing devicewhich tracks the user's browsing history, or by the web browser itself.The browsing history may be combined with other received browsinghistories to create a set of aggregate data used in a similar manner asthe client navigation history described with respect to FIG. 4, topredict a likely navigation event. The received navigation history maybe anonymized to remove any personally identifying information. In someaspects, the received navigation history is received with individualURLs and/or transitional URL pairs provided in a hashed data format toremove any personally identifying information prior to transmission tothe server 104.

At stage 604, the server 104 determines a confidence value for each URLon a particular web page, based on the navigation information receivedat stage 602. For example, the server may employ a method similar tothat disclosed above with respect to FIG. 5 for generating confidencevalues for URLs on a page, except the navigation events are determinedbased upon aggregated data instead of specific user data. As above, theserver 104 may compute confidence values based upon the frequency valuesderived from the navigation information. In some aspects, confidencevalues are determined by the percentage of the time that users selecteda particular navigation event when they were presented with the choiceto select the particular navigation event. The transitional URL pairsprovide for the determination of a confidence value by dividing afrequency value of a source/destination URL pair by a total number ofappearances of the source URL. In some aspects, the server may determinenavigation events based upon transitions from a first page to a secondpage, rather than from a pure visit frequency metric. The server 104 maymaintain an index of web pages and associated URLs and confidence valuesfor each link on the web page, such as in a database. For example, anews site may have five URLs pointing to different news stories. Theserver 104 may receive aggregate data indicating that one of the fivenews stories is selected 60% of the time, with the other four beingselected 10% of the time each. As such, the server 104 would index thepage in a database with a 60% likelihood for the first story link, and10% likelihoods for each of the other four story links.

In some aspects, the server 104 maintains history data in a confidentialmanner, such as by converting each URL to a hash value at stage 606. Inthis manner, the server 104 may provide predicted URL data to a clientdevice without disclosing any personal user data. For example, a usermay visit a banking web page that has a particular user name andpassword login. Depending upon the user, the banking web page mayprovide URLs to each account the user possesses. Each user accessing thepage may have a different set of links provided, depending upon theaccounts the user has with the bank. By converting the links on the pageto non-reversible hash values, the server 104 may provide confidencevalues that are not associable to links on the page unless the user alsopossesses access to the same links (e.g., the client can apply the hashfunction to links they already possess on the currently viewed page todetermine if the confidence values apply). As described above, in someaspects, the hash value is computed by the computing device 200 prior tosending navigation history data to the server 104. In this manner, theserver 104 may receive the navigation history data in the hashed format,without the need to compute a hash value.

At stage 608, the server 104 transmits the hash values and confidencevalues associated with the hash values to a client device, such as thecomputing device 200. The transmittal may be in response to a requestfrom the computing device 200 for a particular URL. In some aspects, theserver 104 may transmit the hash values and confidence values inresponse to a request for such values from a service executing on theclient device 200. For example, when the computing device 200 requeststhe news web page described above, the server 104 provides the hashvalues and confidence values for the five story links present on thatpage. The computing device 200 may also request data for particular linkhash values by first generating a hash value on the client side, thenrequesting a confidence value for the particular hash value from theserver 104.

FIG. 7 is a flow diagram depicting an example method 700 for computing aconfidence value for navigation events associated with a URL using anaggregate navigation history in accordance with aspects of thedisclosure. The method 700 serves to compare navigation events from agiven URL received from a plurality of users, in order to determine howlikely each individual navigation event is. The confidence values may bedetermined in relation to a particular “source” web page, with differentconfidence values for each URL depending upon the page the user iscurrently viewing. For example, the confidence values may be used aboveas described with respect to stage 604 of the method 600 (see FIG. 6).

At stage 702, the server 104 examines received browsing histories andcomputes a number of instances for each navigation event as associatedwith a particular URL. As described above, the instance value may be apercentage or a raw number.

At stage 704, the server 104 may determine if the number of visits tothe URL exceeds a minimum threshold of statistical significance. Forexample, five visits to a particular URL may not provide statisticallysignificant data sufficient to reasonably predict a likely navigationevent away from the URL. For example, if the number of instances of theevent is less than 1000, the server 104 may proceed to stage 710, andnot calculate a probability for the event because the sample size isinsufficient.

At stage 706, the server 104 may determine if a minimum number of usershave submitted data regarding the URL to provide statisticallysignificant data. For example, the method 700 may require that at least50 users have provided data in order to compute and store a confidencevalue for the navigation event. Otherwise the method 700 may proceed tostage 710 and disregard the event until a sufficient number of usershave provided data. As above, the threshold value may fluctuatedepending upon the size of the dataset.

At stage 708, the server 104 determines a window size of recentinstances. The window size refers to the number of latest visits to theURL that will be examined to determine the confidence value, or a lengthof time to search back through the instances. The window size may bedetermined based on the amount of traffic the URL receives, how oftenthe content of the URL changes. For example, a news website that hasconstantly changing content might require a small instance window,because links from the regularly changing URL would grow stale. Awebsite with a small amount of traffic would typically require a longerwindow size in order to gather enough results for statisticalsignificance. The window size might be set at 50 instances, 100instances, 1000 instances, all instances within the last hour, withinthe last day, within the last week, or the like.

At stage 712, the server 104 computes the number of times eachparticular navigation event, such as the next URL visited for thecurrent URL, occurs within the instances defined by the window sizedetermined at stage 710. For example, out of 1000 visits to a newswebsite, a particular article might be selected 600 times, resulting ina confidence value of 60% for navigating to that article from the URL.

While the present example primarily relates to determination of anavigation event based upon a number of accesses as a percentage oftotal navigation events, additional heuristics may also be used toderive the likely event based upon information supplied by the user,such as the previous navigation event (e.g., the website that led to thecurrently analyzed URL), the time of day (e.g., users are more likely tocheck news sites when in the morning when they arrive at work), theuser's location (e.g., users in a particular geographic region arelikely to check sports scores for local teams), or other demographicinformation.

At stage 714, the server 104 optionally compares the confidence valuesfor the navigations events from the URL with a threshold value. If theconfidence values do not meet the threshold value, the server 104 mayidentify a subset of available navigation events, as possible predictedlikely navigation events. In this manner the server 104 avoidspredicting navigation events when the event does not have astatistically significant likelihood of occurring, thus potentiallysaving bandwidth on prerender operations on pages that are unlikely tobe visited. The threshold may be set at a variety of different values,such as 5%, 25%, 50%, or 75%. In some aspects, the threshold may bedynamically altered based upon the number of navigation links present atthe URL, the type of URL, the traffic of the URL, the speed at whichcontent changes at the URL, and the like. If the confidence values donot meet the minimum threshold, the server 104 may filter out thepossible events that do not meet the minimum threshold.

If the navigation event or events meet the minimum threshold, or themethod 700 does not check for a minimum threshold, the most likelynavigation event or events and the likelihood for each event are storedalong with the URL at stage 716. The navigation events and confidencevalues may be supplied in response to a request to the user, such asoccurs at stage 608 described with respect to FIG. 6. The method 700ends after computing and storing the confidence values for thenavigation events associated with the URL.

FIG. 8A is a flow diagram depicting an example method 800 for predictinga navigation event based on an aggregate navigation history using hashvalues to anonymously manage link data in accordance with aspects of thedisclosure. The method 800 provides logic by which a computing device200 may predict a navigation event based upon data received from aserver 104, such as the data generated by the method 700 described withrespect to FIG. 7.

At stage 802, the computing device 200 receives a set of data from aremote server 104, the set of data comprising information associatedwith an aggregate browsing history of a web page. This aggregate datamay be received in response to a request made by the computing device200 in response to navigating to a particular web page. The aggregatedata may represent a collection of data received by a remote server froma plurality of users. For example, a web browser plug-in may allow theuser to “opt-in/out” of functionality that may send their anonymizednavigation history to a remote server. The remote server may thencollect navigation histories from a plurality of users, stored as anaggregate navigation history, such as described above (see FIG. 7). Forexample, the navigation prediction module 208 may generate a request tothe server 104 every time the user navigates to a web page, for theaggregate browsing data associated with that web page. The navigationprediction module 208 may then predict a likely next navigation eventusing the received data, so as to supply the prerender module with anext page to prerender to improve the browsing experience.

Due to the data's aggregate nature, it can be provided as a series ofhash values to protect individual user information, as described abovewith respect to FIG. 6. As such, the computing device 200 associates thereceived hash values and confidence values with the links present on thecurrent URL. To begin this process, at stage 804, the computing devicecomputes a hash value for each link on the current page using the samehash function as used by the server 104 to anonymize the link data. Asdescribed above, in some aspects the hash value is computed on thecomputing device prior to sending navigation history data to the server.In such cases, the hash value would match the original computed valuedetermined by the computing device prior to the navigation event beingtransmitted to the server, rather than a value computed on the server.

At stage 806, the computing device 200 compares the computed hash valueswith the received hash values from the server 104. In this manner, thecomputing device 200 may match the confidence values and hash valuesreceived from the server 104 with the links available for the user toselect on the currently viewed web page. The confidence values indicatea likelihood that a particular navigation event associated with the hashvalue will be selected. The computing device 200 may thus map thecurrently viewable links with the received confidence values.

At stage 808, the computing device 200 identifies the link or links withthe highest confidence value or values as the predicted next navigationevent. The method 800 ends after predicting the next navigation event.

FIG. 8B is an illustration of an example interface 810 of a web browseremploying an example method for predicting a user navigation event basedon a navigation history in accordance with aspects of the disclosure.The illustration depicts a web browser interface 810 displaying a website and a set of navigation history data 812. The web page 810comprises one or more links 814, 816, 818, 820. These links 814, 816,818, 820 may be URLs that, when selected by a user, direct the webbrowser to display a set of content associated with the selected link.

The navigation history data 812 comprises data associated with the links814, 816, 818, and two other links, Link E and Link F that are notpresent for the current user viewing the page. The navigation historydata 812 may represent an analysis of the individual user's navigationhistory (See FIGS. 4-5, 9-11), or an aggregate navigation history (SeeFIGS. 6-11). The navigation history 812 comprises information about thelinks 814, 816, 818, and a confidence value associated with each link.

The navigation history 812 may be used by other aspects of a computingdevice 200, such as the navigation prediction module 208, to predict thenext navigation event. For example, in the present illustration,according to the navigation history 812, there is a 30% chance the userwill select Link A 814, a 60% chance the user will select Link B 816,and a 5% chance the user will select Link C 818. Link D 820 does nothave any associated data stored in the navigation history 812. The lackof data for Link D 820 may be explained in a variety of manners, such asthat the chance of selection of Link D 820 is below a threshold value,or that no data has been submitted for Link D 820. The navigationhistory 812 also displays a non-zero chance of selecting two links thatare not present, Link E and Link F. These links may have been removedfrom the web page in an update, or they may not be visible to all users,such as the user currently accessing the page. In accordance withaspects of the disclosure, the navigation prediction module 208identifies Link B 814 as a predicted next navigation event because theconfidence value of Link B 814 is greater than the values for Link A 812and Link C 818.

FIG. 9 illustrates an example of directed graph for storing navigationevents. This graph shows one possible implementation of the clientnavigation history 212 as described above (see FIG. 2). The graphdepicted in FIG. 9 provides a reference to each navigation event (e.g.,a website URL) as a vertex in the graph, with the edges weighted by thenumber of visits to a destination navigation event from a sourceaddress. As new navigation events occur, vertices are added to thegraph, edge weights are updated, and new edges are generated.

The directed graph may be associated with a particular user, aparticular group or subset of users, all users in aggregate, or anyother division as appropriate for predicting future navigation events.The directed graph may be stored locally or remotely, as described abovewith respect to client navigation histories and aggregate navigationhistories (see FIGS. 2, 4-8A). As described above, navigation historiesmaintained remotely may be maintained in an opt-in manner, requiringusers to explicitly allow their navigation history to be stored for usein prediction of navigation events. Data maintained in this manner maybe anonymized and depersonalized in order to protect user privacy.

Although the depicted example graph shows relationships between pairs ofnavigation addresses, higher order graphs may be constructed from a mapwhere the key is the last “n” visited navigation events and the value isan array of pairs of destination addresses and counts. For example, afirst order model might depict a single address pair transition (e.g.,address A->address B) to predict the first destination, while a secondorder model might by keyed using a pair of source addresses, and predicta third address as a destination (e.g., when the user starts at addressA, and then navigates to address B, what is the likely next event?).Higher order models are also possible, with increasingly lengthynavigation histories being examined to determine the likely navigationevent. In the present example, the directed graph depicted in FIG. 9 isassociated with a two part navigation history (e.g., “n” is equal totwo), where the site previously viewed is “URL F,” and the current siteis the source vertex in the graph (e.g., URL A, B, C, D, or E).

Examination of longer navigation histories (e.g., higher numbers of “n”previous events examined) may be more probative of user behavior, due tothe ability to more closely match past observations to currentconditions. As such, navigation prediction operations may be weighted bythe length of the history examined. Prediction operations may alsorequire a minimum number of occurrences, or they may be weighted by thenumber of occurrences in order to prevent a long navigation history witha single instance from overriding a shorter history with an overwhelmingdata count.

Prediction operations may also be performed using a subset of the recentnavigation history. For example, a set of previously viewed pages A, B,and C may be indicative of a certain navigation pattern (e.g., after theuser visits A, B, and C in any particular order, the user then tends tovisit site D). Thus, a user visiting sites A, E, F, C, G, B (in thatorder) may use the information associated with the “loose” set ofpreviously viewed pages A, B, and C because each of A, B, and C arepresent within the most recent navigation history, allowing foradditional sites to be accessed in between, or modifications in thenavigation order. An example of a method for predicting a likelynavigation event using a directed graph is described below (see FIG.11).

FIG. 10 is an illustration of an example of a method 1000 for building adirected graph in accordance with aspects of the disclosure. Asnavigation events occur, they are added to the directed graph for use innavigation prediction operations. Connections between vertices of thegraph (e.g., website addresses) are weighted by the number of times thebrowser navigates from the source vertex (e.g., the source websiteaddress) to the destination vertex (e.g., the destination websiteaddress).

At stage 1002, a navigation event is received. For example, a link maybe selected from a website, e-mail, or instant message client, a URL maybe typed into an address bar, or another application may initiate anavigation operation (e.g., a selection of a search result provided by aseparate search application).

At stage 1004, a determination is made as to whether the receivednavigation event corresponds to an entry within a graph. As describedabove with respect to FIG. 9, the navigation event used as the key toidentify a graph may be a plurality of vertices representing a recentnavigation history, rather than a single navigation event.

If the navigation event is not present within the directed graph, a newvertex for the navigation event is added at stage 1006. Otherwise, themethod proceeds to stage 1008.

At stage 1008, the counter value defining the weight of an edge of thegraph between the current or source URL and the destination URL isincremented. In a directed graph with greater depth than a singlenavigation event, an edge from the source “n” navigation events to thedestination navigation event may be incremented. In this manner,navigation events between vertices of the graph are stored in theweights of the edges of the directed graph.

FIG. 11 is an illustration of an example of a method 1100 for predictinga navigation event using a directed graph in accordance with aspects ofthe disclosure. Once created, the directed graph may be used to predictfuture navigation events based on a past navigation history.

At stage 1102, the past “n” navigation events in a navigation historyare determined. For example, the most recent web pages visited by theuser may be identified, in the particular order in which the uservisited them. A navigation event history may be identified for aparticular web browser tab or instance, or across all active browsertabs or instances.

At stage 1104, the navigation event history determined at stage 1102 ismapped to a navigation history data graph. The navigation event historymay be mapped to multiple data graphs, depending upon the length of thehistory and the “n” order depth of the graphs. For example, a navigationhistory of URL A->URL B->URL C->URL D may map to up to four separatedirected graphs, a graph for URL D, a graph for URL C->URL D, a graphfor URL B->URL C->URL D, and a graph for URL A->URL B->URL C->URL D.

At stage 1106, a confidence value is determined for the potentialdestination addresses within the directed graphs determined at stage1104. The potential destination addresses associated with each directedgraph may also be compared against the set of addresses present on acurrent web page. For example, confidence values may not be identifiedfor links that are not active and selectable on the web page the user isviewing.

The confidence value may be determined using the weights of the edgesbetween the source vertex of the directed graph (e.g., the currentlyviewed page) and each destination vertex linked to the source vertex.The confidence value may be the probability of each navigation event(e.g., the count weight of the possible navigation event divided by thetotal count of potential next navigation events), weighted such thathigher-n entries are given more weight.

Deeper graphs tend to provide more specific data, but graphs with moredata points are better because they are more representative. As such, itmay be appropriate to use a formula to determine a weight for eachprobability given the tradeoffs between different probabilitycalculations. For example, if a data set has 5 graphs of depths 1through 5, a probability value from each graph might be weighted usingthe graph depth and the number of samples in the graph. One possibleformula for determining the weight of each probability value may be:weight=graph_depth^(N)*samples_in_graph  (Eq. 1)

Graph_depth is the number of navigation events used as the key to thegraph, samples_in_graph is the number of navigation events stored in thegraph, and N is a number greater than 1 used to configure the biastowards graph depth instead of sample size. This formula may determinethe weight or significance of each graph, and the probabilities for eachURL from each graph may be determined according to that weight.

At stage 1108, one or more likely navigation events are identified usingthe confidence values determined at stage 1106. For example, all eventswith a confidence value greater than a threshold value may be identifiedas likely navigation events (e.g., all navigation events with at least a50% confidence value, at least a 75% confidence value, or at least a 90%confidence value), a certain number of events with top confidence valuesmay be identified as likely navigation events, or a top navigation eventfrom each examined directed graph may be identified as a likelynavigation event.

At stage 1110, one or more likely navigation events are prerendered.Although the present example describes the use of likely navigationevents for prerendering, other actions may also be taken to assist theuser with accessing the content associated with the likely navigationevent. For example, the likely navigation events may be presented to theuser in a separate interface window for selection, or variousprefetching processes may be enabled based upon the probability of eachnavigation event. In some aspects, increasingly aggressive processes areperformed in response to the probability of selection of the navigationevent. For example, a given navigation event may be prerendered atgreater than 90% probability, but only domain name services (DNS)information precached at 60% probability, and no action taken at all atbelow 10% probability.

The stages of the illustrated methods described above are not intendedto be limiting. The functionality of the methods may exist in a fewer orgreater number of stages than what is shown and, even with the depictedmethods, the particular order of events may be different from what isshown in the figures.

The systems and methods described above advantageously provide for animproved browsing experience. By predicting the next navigation event,the browser can perform prerender operations to minimize the amount oftime users wait for web pages to load. Multiple methods to perform theprerender operations provide a flexible and robust system fordetermining the next navigation event.

As these and other variations and combinations of the features discussedabove can be utilized without departing from the disclosure as definedby the claims, the foregoing description of the embodiments should betaken by way of illustration rather than by way of limitation of thedisclosure as defined by the claims. It will also be understood that theprovision of examples of the disclosure (as well as clauses phrased as“such as,” “e.g.”, “including” and the like) should not be interpretedas limiting the disclosure to the specific examples; rather, theexamples are intended to illustrate only some of many possibleembodiments.

The invention claimed is:
 1. A computer-implemented method forpredicting a user navigation event, the method comprising: storing a setof navigation data, the navigation data comprising at least one directedgraph, where at least one vertex of the directed graph comprises atleast one navigation event and an edge of the directed graph is weightedby a count value representing a number of times a navigation hasoccurred from a source vertex to a destination vertex coupled thereto bythe edge; identifying a current navigation history, the currentnavigation history comprising one or more previous navigation eventsaccessed in a browser; mapping the current navigation history, using aprocessor, to the at least one directed graph within the set ofnavigation data; determining a confidence value for a given navigationevent using the at least one directed graph; and identifying the givennavigation event as a likely navigation event based on the confidencevalue, such that the likely navigation event is configured to beutilized by the browser to assist with navigation operations.
 2. Themethod of claim 1, wherein the at least one directed graph is keyed toat least one previous navigation event in the current navigationhistory.
 3. The method of claim 2, wherein the at least one previousnavigation event is identified in an order each of the at least oneprevious navigation events were visited.
 4. The method of claim 1,further comprising: identifying n previous navigation events in thecurrent navigation history; and determining which of the at least onedirected graphs is keyed to the n previous navigation events.
 5. Themethod of claim 1, wherein the confidence value is weighted by at leasta depth of the at least one directed graph.
 6. The method of claim 5,wherein a weight of the directed graph is determined by a formulagraph_depth^(N)*samples_in_graph, wherein graph_depth is the depth,samples_in_graph is a number of navigation events stored in the graph,and N is a numerical value used to adjust the significance of thegraph_depth term in the weight calculation.
 7. The method of claim 1,further comprising prerendering the likely navigation event.
 8. Themethod of claim 1, wherein the browser is a web browser.
 9. Anon-transitory computer-readable storage medium comprising instructionsthat, when executed by a processor, cause the processor to perform amethod comprising: storing a set of navigation data, the navigation datacomprising at least one directed graph, where at least one vertex of thedirected graph comprises at least one navigation event and an edge ofthe directed graph is weighted by a count value representing a number oftimes a navigation has occurred from a source vertex to a destinationvertex coupled thereto by the edge; identifying a current navigationhistory, the current navigation history comprising one or more previousnavigation events accessed in a browser; mapping the current navigationhistory to the at least one directed graph within the set of navigationdata; determining a confidence value for a given navigation event usingthe at least one directed graph; and identifying the given navigationevent as a likely navigation event based on the confidence value, suchthat the likely navigation event is configured to be utilized by thebrowser to assist with navigation operations.
 10. The non-transitorycomputer readable medium of claim 9, wherein the at least one directedgraph is keyed to at least one previous navigation event in the currentnavigation history.
 11. The non-transitory computer readable medium ofclaim 10, wherein the at least one previous navigation event isidentified in an order each of the at least one previous navigationevents were visited.
 12. The non-transitory computer readable medium ofclaim 9, further comprising: identifying n previous navigation events inthe current navigation history; and determining which of the at leastone directed graphs is keyed to the n previous navigation events. 13.The non-transitory computer readable medium of claim 9, wherein theconfidence value is weighted by a depth of the at least one directedgraph.
 14. The non-transitory computer readable medium of claim 9,further comprising prerendering the likely navigation event.
 15. Aprocessing system for predicting a user navigation event, the processingsystem comprising: at least one processor; and a memory, coupled to theprocessor, for storing a set of navigation data, the navigation datacomprising at least one directed graph, where at least one vertex of thedirected graph comprises at least one navigation event and an edge ofthe directed graph is weighted by a count value representing a number oftimes a navigation has occurred from a source vertex to a destinationvertex coupled by the edge; wherein the processor is configured to:identify a current navigation history, the current navigation historycomprising one or more previous navigation events accessed in a browser;map the current navigation history to the at least one directed graphwithin the set of navigation data; determine a confidence value for agiven navigation event using the at least one directed graph; andidentify the given navigation event as a likely navigation event basedon the confidence value, such that the likely navigation event isconfigured to be utilized by the browser to assist with navigationoperations.
 16. The processing system of claim 15, wherein the browseris configured to prerender the likely navigation event.
 17. Theprocessing system of claim 15, wherein the at least one directed graphis keyed to at least one previous navigation event in the currentnavigation history.
 18. The processing system of claim 17, wherein theat least one previous navigation event is identified in an order each ofthe at least one previous navigation events were visited.
 19. Theprocessing system of claim 15, wherein the processor is furtherconfigured to: identify n previous navigation events in the currentnavigation history; and determine which of the at least one directedgraphs is keyed to the n previous navigation events.
 20. The processingsystem of claim 15, wherein the confidence value is weighted by a depthof the at least one directed graph.
 21. The processing system of claim15, wherein the browser is a web browser.